Testing and tracking in the UK: A dynamic causal modelling study

Area of Science:

• Epidemiology
• Mathematical Modelling
• Public Health

Background:

• The COVID-19 pandemic necessitated rapid development of predictive models.
• Understanding the impact of public health interventions like testing and isolation is crucial for managing outbreaks.

Purpose of the Study:

• To model the effects of self-isolation following COVID-19 testing and tracking.
• To simulate the trajectory of the COVID-19 outbreak in the UK over 18 months.
• To evaluate the impact of different public health strategies on infection rates and mortality.

Main Methods:

• A dynamic causal model of COVID-19 was enhanced with an isolation state.
• Maximum a posteriori estimates of model parameters were derived using UK time series data (cases, deaths, tests).
• The model was used to simulate outbreak trajectories under various scenarios.

Main Results:

• Relaxation of social distancing is unlikely to cause a significant rebound in infections.
• The emergence of a second COVID-19 wave is primarily dependent on waning immunity.
• Testing strategies can delay, but not eliminate, a second wave and do not reduce mortality rates.

Conclusions:

• A testing and tracking policy can defer a second COVID-19 wave beyond 18 months within current capabilities.
• Effective tracing and tracking require approximately 20% efficacy for asymptomatic cases and 50,000 tests daily.
• Model validation was performed using comparative analysis between the UK and Germany, supplemented by serological studies.